Cascades occur and are ubiquitous in various connected environments. For example, information cascades can occur in a social media setting when posts or tweets are spread rapidly over a computer network. Similarly, cascades can occur in economic markets, e.g., involving the buying and selling of stock, in transportation, e.g., involving the flow of traffic, in healthcare, e.g., involving the spread of a disease, etc. Regardless of the context, it is very difficult to identify and predict cascades.
Nonetheless, identifying, understanding and predicting how a cascade will behave can be of great value. For example, in cases where a cascade can cause interruptions, resources can be allocated or reallocated to mitigate such problems. While there has been a fair amount of work focused on determining a final cascade size, little has been done to predict cascades at different time intervals, i.e., provide a model of the cascading process.